• BMB Reports
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• 제49권12호
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• pp.671-680
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• 2016

Accumulating evidence indicates many brain functions are mediated by epigenetic regulation of neural genes, and their dysregulations result in neuronal disorders. Experiences such as learning and recall, as well as physical exercise, induce neuronal activation through epigenetic modifications and by changing the noncoding RNA profiles. Animal models, brain samples from patients, and the development of diverse analytical methods have broadened our understanding of epigenetic regulation in the brain. Diverse and specific epigenetic changes are suggested to correlate with neuronal development, learning and memory, aging and age-related neuronal diseases. Although the results show some discrepancies, a careful comparison of the data (including methods, regions and conditions examined) would clarify the problems confronted in understanding epigenetic regulation in the brain.

• The Journal of Korean Physical Therapy
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• 제1권1호
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• pp.47-61
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• 1989

This paper presents the event approach to motor skill acquisition as a theoretical treatment of the learning and relearning of motor skill. 1) The use of norm-referenced developmental assesment tools and standardized qualitative assessment tool is an important component of infant movement evaluation. 2) The kinesthetic modality relaying movement and position imformation to the central nervous system is important for the detection and corretion of movement error. 3) The event approach treats the actor and the environment as inseparable in the acquisition of skills. 4) Motoy learning focuses almost entirely on how the skill is learned, contRolled and reTained. 5) Developmental assessment have needed an assessment of motor development. 6) A significant difference was found between articulation disorders children and motor coordination problem. 7) verbal ability is not essential for the learning of motor skills. 8) The Control of motor skills is a cognitive ability.

• 스마트미디어저널
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• 제9권3호
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• pp.9-17
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• 2020

Hippocampus is a complex brain structure embedded deep into the temporal lobe. Studies have shown that this structure gets affected by neurological and psychiatric disorders and it is a significant landmark for diagnosing neurodegenerative diseases. Hippocampus features play very significant roles in region-of-interest based analysis for disease diagnosis and prognosis. In this study, we have attempted to learn the embeddings of this important biomarker. As conventional metric learning methods for feature embedding is known to lacking in capturing semantic similarity among the data under study, we have trained deep Siamese convolutional neural network for learning metric of the hippocampus. We have exploited Gwangju Alzheimer's and Related Dementia cohort data set in our study. The input to the network was pairs of three-view patches (TVPs) of size 32 × 32 × 3. The positive samples were taken from the vicinity of a specified landmark for the hippocampus and negative samples were taken from random locations of the brain excluding hippocampi regions. We have achieved 98.72% accuracy in verifying hippocampus TVPs.

• International journal of advanced smart convergence
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• 제10권2호
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• pp.73-78
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• 2021

Recently, the number of people who visit the hospital due to diabetes is increasing. According to the Korean Diabetes Association, it is statistically indicated that one in seven adults aged 30 years or older in Korea suffers from diabetes, and it is expected to be more if the pre-diabetes, fasting blood sugar disorders, are combined. In the last study, the validity of Triglyceride and Cholesterol associated with diabetes was confirmed and analyzed using Random Forest. Random Forest has a disadvantage that as the amount of data increases, it uses more memory and slows down the speed. Therefore, in this paper, we compared and analyzed Random Forest and XGBoost, focusing on improvement of learning speed and prevention of memory waste, which are mainly dealt with in machine learning. Using XGBoost, the problem of slowing down and wasting memory was solved, and the accuracy of the diabetes recognition factor was further increased.

• ETRI Journal
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• 제45권6호
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• pp.1007-1021
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• 2023

Stroke is the leading cause of permanent disability in adults, and it can cause permanent brain damage. According to the World Health Organization, 795 000 Americans experience a new or recurrent stroke each year. Early detection of medical disorders, for example, strokes, can minimize the disabling effects. Thus, in this paper, we consider various risk factors that contribute to the occurrence of stoke and machine learning algorithms, for example, the decision tree, random forest, and naive Bayes algorithms, on patient characteristics survey data to achieve high prediction accuracy. We also consider the semisupervised self-training technique to predict the risk of stroke. We then consider the near-miss undersampling technique, which can select only instances in larger classes with the smaller class instances. Experimental results demonstrate that the proposed method obtains an accuracy of approximately 98.83% at low cost, which is significantly higher and more reliable compared with the compared techniques.

• Journal of Sleep Medicine
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• 제15권2호
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• pp.48-54
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• 2018

Objectives: The aim of this study was to develop a predicting model for the optimal continuous positive airway pressure (CPAP) for obstructive sleep apnea (OSA) patient with obesity by using a machine learning. Methods: We retrospectively investigated the medical records of 162 OSA patients who had obesity [body mass index (BMI) ≥ 25] and undertaken successful CPAP titration study. We divided the data to a training set (90%) and a test set (10%), randomly. We made a random forest model and a least absolute shrinkage and selection operator (lasso) regression model to predict the optimal pressure by using the training set, and then applied our models and previous reported equations to the test set. To compare the fitness of each models, we used a correlation coefficient (CC) and a mean absolute error (MAE). Results: The random forest model showed the best performance {CC 0.78 [95% confidence interval (CI) 0.43-0.93], MAE 1.20}. The lasso regression model also showed the improved result [CC 0.78 (95% CI 0.42-0.93), MAE 1.26] compared to the Hoffstein equation [CC 0.68 (95% CI 0.23-0.89), MAE 1.34] and the Choi's equation [CC 0.72 (95% CI 0.30-0.90), MAE 1.40]. Conclusions: Our random forest model and lasso model ($26.213+0.084{\times}BMI+0.004{\times}$apnea-hypopnea index+$0.004{\times}oxygen$ desaturation index-$0.215{\times}mean$ oxygen saturation) showed the improved performance compared to the previous reported equations. The further study for other subgroup or phenotype of OSA is required.

• 산업경영시스템학회지
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• 제32권4호
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• pp.215-222
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• 2009

Human voice reacts very sensitively to human's minute physical condition. For instance, human voice disorders affect patients profoundly especially in the case of Parkinson's disease. Acoustic tools such as MDVP, can function as an equipment that measures various voice in different objects. Many different approaches have been applied for analyzing the voice disorders for diagnosis of Parkinson's disease. According to the voice data of suspected Parkinson's patients from UCI Machine Learning Repository, it is reported to have 23 people with Parkinson's disease and 8 healthy people. Applying Mahalanobis Taguchi System (MTS) for diagnosis of Parkinson's disease, the correct diagnosis performance is compared to previous research results.

• 디지털콘텐츠학회 논문지
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• 제18권1호
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• pp.35-46
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• 2017

본 연구는 자폐 스펙트럼 장애 학생을 대상으로 하는 증강현실기반 교육 콘텐츠 연구들을 고찰해 보면서 연구동향과 시사점을 탐색하는 것을 목적으로 하였다. 이를 위해 학술 데이터베이스를 활용하여 2006년에서 2016년까지 발표된 논문들을 수집한 후, 선정기준에 적합한 12편의 국외 연구들을 분석 대상으로 선정하였다. 연구결과, 선행연구들은 초등학생들을 대상으로한 연구가 많았으며, 연구설계는 단일대상연구가 가장 많았다. 연구도구로는 증강현실을 위한 모바일 기기가 다수를 차지하고 있었으며, 측정방법으로는 행동관찰방법을 많이 사용하였다. 증강현실기반 교육콘텐츠 유형은 사물조작형, 자기모델링 관찰조작형, 현장문제해결형, 위치기반 학습안내형으로 구분되었다. 교육적 효과는 사회적 행동, 놀이 및 모방행동, 타인에 대한 감정인식의 향상이 주로 나타났다. 마지막으로, 자폐 스펙트럼 장애 학생을 위한 증강현실기반 교육 콘텐츠 개발과 적용을 위한 고려사항을 도출하여 제시하였다.

• 한국컴퓨터정보학회논문지
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• 제25권8호
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• pp.47-54
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• 2020

발달장애는 영유아 기부터 시작하는 뇌 신경계 발달장애들의 집합으로 언어 및 의사소통, 인지력, 사회성 등의 측면에서 이루어져야 할 발달이 심하게 지체되거나 성취되지 않은 장애를 의미한다. 이러한 발달장애 진단에는 아동의 얼굴 표정과 같은 감정표현의 의미와 맥락 등 비언어적 반응에 대한 관찰로 이루어진다. 이를 사람이 측정기에는 상당히 주관적인 판단이 개입하게 되어 객관적인 기술이 필요하다. 따라서 본 연구에서는 영유아/아동의 언어, 비언어적 행동 반응을 관찰하는 ADOS(Autism Diagnostic Observation Schedule)와 BeDevel(Behavior Development Screening for Toddler) 검사에서 검사자와 피검사자간의 상호작용이 녹화된 영상을 리빙랩 환경에서 획득하여 인공지능 기반의 비정상적/상동적 행동 인지 기술 개발에 필요한 영상 및 음성 데이터 확보를 목표로 한다.

• The Korean Journal of Physiology and Pharmacology
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• 제24권2호
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• pp.129-135
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• 2020

SHP2 is an unusual protein phosphatase that functions as an activator for several signaling pathways, including the RAS pathway, while most other phosphatases suppress their downstream signaling cascades. The physiological and pathophysiological roles of SHP2 have been extensively studied in the field of cancer research. Mutations in the PTPN11 gene which encodes SHP2 are also highly associated with developmental disorders, such as Noonan syndrome (NS), and cognitive deficits including learning disabilities are common among NS patients. However, the molecular and cellular mechanism by which SHP2 is involved in cognitive functions is not well understood. Recent studies using SHP2 mutant mice or pharmacological inhibitors have shown that SHP2 plays critical role in learning and memory and synaptic plasticity. Here, we review the recent studies demonstrating that SHP2 is involved in synaptic plasticity, and learning and memory, by the regulation of the expression and/or function of glutamate receptors. We suggest that each cell type may have distinct paths connecting the dots between SHP2 and glutamate receptors, and these paths may also change with aging.